In this work, we try to develop a fast converging method for
segmentation assisted deformable registration. The segmentation step
consists of a piece-wise constant Mumford-Shah energy model while reg-
istration is driven by the sum of squared distances of both initial images
and segmented mask with a diffusion regularization. In order to solve
this energy minimization problem, a second order Gauss-Newton opti-
mization method is used. For the numerical experiments we used CT
data sets from the EMPIRE10 challenge. In this preliminary study, we
show high accuracy of our algorithm.